prof dr edwin cuppen, umc utrecht and hubrecht institute

PERSONALIZED CANCER TREATMENTProf dr Edwin Cuppen, UMC Utrecht and Hubrecht Institute

11/6/2014 © 2012–2014 Healthcare Information and Management Systems Society (HIMSS) 2

organism cell chromosome DNA

Deoxyribo Nucleic Acid (DNA)

Changes in DNA (mutations) can cause disease- Early in embryogenesis: congenital disease- In somatic tissue: cancer

~3 billion letters: G, A, T, C

1,000 dollar genome: January 2014

Applications of Personal Genomes in Clinical CareFrom cradle to grave / From pre-womb to tomb

-2

Birth planning- Screening carriership

-0.5

Pregnancy- NIPT (trisomy 13/18/21, gender, carriership)

Newborn- Replacing heel prick: detectionrare congenital disease

0 0-10

Diagnostics congenital disease- De novo mutation screening- Whole genome scan

10-20

Disease prevention- BRCA, CFTR

Cancer- Personalized treatment

>50

Pharmacogenetics-drug/dose choice

60

Aging- Understanding healthyaging

>100

Death- Genetic autopsy unexplaineddisease cause

Changes in DNA are the basis for cancer

But also make every cancer patient unique

Personalized treatment

Tumor growth requires changes of multiple characteristics

- Drugs have been or are being designed to target various biological processes- Many drugs only work in part of patients

- No biomarkers are available for most drug sensitivity or resistance

Biomarker discovery requires large cohorts

and systematic integration of genetic and treatment data

founded in 2010: UMCU, EUR, NKI

UMC Groningen

VuMC AmsterdamMeander

MUMC Maastricht

Radboud Nijmegen

LUMC Leiden

AMC Amsterdam

Center for Personalized Cancer Treatment (www.cpct.nl)

Personalized Cancer Treatment

Obtain patient biopsy

Bioinformatics and Systems Biology to identify affected pathways and select drugs

Treat patient with selected drug(s)

until disease progression

Patient-Centered Analysis

Longitudinal data monitoring system allows observation of patient’s molecular and clinical changes over time, as new conditions develop, drugs administered and lab tests taken

Medications,Surgeries, etc.

Events -Metastases

Samples & Molecular Data

Lab measurements

Validated Clinically-Actionable Markers

Additional Markers with Potential Clinical

Benefit

Patient-Centered Reporting

Generating individual patient reports for clinicians summarizing prognostic and predictive markers identified in patient’s sample

Gene X mutated

Gene X wild type

Systematic biomarker discovery

Identify DNA changes associated with good or poor response

Treatment Z

Patientdata

Treatmentand respons

Pathologyand lab

DNA sequences

eZIS/EPD eCRF LMSpathology

Research DB

Patient report

Biomarker discovery

Publicdata

SYSTEMATIC DATA INTEGRATION AND MINING

PalGAIKNL, etc

Medical Specialist

Stak

e-h

old

ers

PatientFuture patient

Insurance company

Pharma Society

‘BIG data’ challenges Systematic and large scale data collection is valuable for improving quality and

efficacy of care

Personalized cancer treatment is already possible

Only for some agents/indications: need for routine diagnostic testing

Systematic data collection and research required for others

Footprints one-dimensional datasets are large

ICT infrastructure

Whole genome information part of EMR

Multi-dimensional integration is required

coupling of heterogeneous data sources

use of standards/ontologies

Data security and data access needs to be guaranteed

Safety and misuse

Who owns this information